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| The permutation flow shop problem (PFSSP) is a well-known difficult combinatorial optimization problem. In this paper, we present a new hybrid optimization algorithm named OHGSA to solve the PFSSP. First, to make GSA suitable for PFSSP, a new LRV rule based on random key is introduced to convert the continuous position in GSA to the discrete job permutation. Second, The NEH heuristic was combined the random initialization to initialize the population with certain quality and diversity. Third, to improve the convergence rate of GSA, the opposition-based DE employs opposition-based learning for the initialization and for generation jumping to enhance the global optimal solution. Fourth, the fast local search is used for enhancing the individuals with a certain probability. Additional-ly, Comparison with other results in the literature shows that the OHGSA is an ef-ficient and effective approach for the PFSSP. |
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| Keywords:computer application technology;gravitational search algorithm; permutation flow shop scheduling |
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